Geocoding in R
Patty Frontiera
Fall 2018
Geocoding in R
Download the zipfile from https://github.com/dlab-geo/RGeocoding
unzip the files
start RStudio
start a new R script file
set your working directory to the location of the unzipped files
Introductions
About me
About you - why are you here?
Workshop Overview
- Intro to Geocoding
- Review of Geocoding tools
- Geocoding in R with
ggmap - Geocoing in R with
ESRI World Geocoding Service
What is Geocoding
Obtain geographic coordinates for a named place, street address, or zip code.
Types of places you can Geocode
- state, city, building,
- street address or intersection,
- named place (eg mountain), landmark,
- crime or other event location,
- zip code, etc.
Why Geocode?
Display locations on a map
Link locations to other data
- Spatial analysis
- Calculate distance, direction, area, etc.
- Identify patterns & relationships:
- clusters, outliers, neighbors
Geocoding Components
File of Input locations
- places of the same type in a standard format
A Geocoder
- Software and a reference database
- The software compares input locations to database & returns matches
Output file
- Geographic coordinates for each matched input location
- Metadata about the match
Reference Databases
What types of data are available/ should be used for matching
- Rooftop
- Parcel
- Street Addresses
- Street Address Range
- Street, City, Zipcode, State
Dual Address Range
Where do you think the point for 275 5th St NE would be located?
Dual Address Range
Where do you think the point for 275 5th St NE would be located?
Parcel & Rooftop Reference Database
Addresses would match to centroid of parcel or rooftop
Reference Database Quality
Geocode with maps.google.com
Try entering a city, landmark (Sather Gate), zipcode, address, etc.
Geocoding Workflow
- Consider your needs & options
- Choose geocoder
- Preprocess / clean your input data
- Geocode
- Evaluate your output
- Postprocess as needed
Considerations
- Geographic & temporal scope
- Output type and quality
- Volume / speed
- Security
- Costs of software and data
- Ease of use
Geocoding Options
- US Census Geocoding Tool
- ESRI
- ArcGIS/ArcPro Desktop software
- ArcGIS Online Platform
- World Geocoding Service API
Local Geocoding
Needed when
- Input data are confidential or too large
You can use ESRI ArcGIS/ArcPro with local reference database
- you need to install local reference database
- limited to USA & Canada
- needs to be updated manuallyAvailable in the D-Lab
Geocoding Practice
Open these geocoding tools in 3 different browser windows
Compare results
Geocode the following address in all three tools:
2625 Dana St, Berkeley, CA, 94704
Geocoding in R
Geocoding with ggmap
This has been the most common R package for geocoding.
But, beginning July 2018 Google changed its pricing model.
For API access you need to register a credit card with Google
$200 worth of free API services per month
That’s 40,000 geocoded locations
See the Google Geocoding API Documentation for details
Geocoding with ggmap
Worth the trouble because output is great quality.
But only if you can afford the costs!
Let’s try it.
ggmap
Load the library
library(ggmap)## Loading required package: ggplot2## Google Maps API Terms of Service: http://developers.google.com/maps/terms.## Please cite ggmap if you use it: see citation("ggmap") for details.ggmap
Register your API key
#mykey <- "AIzaSyxxxxxxxxxxxxxxxxxxxxxxxxxOQyOFWrTw"
mykey <- "AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw"
register_google(key=mykey)Geocode a city
geocode("San Francisco, CA")Geocode a state
geocode("California")Geocode a landmark
geocode("Golden Gate Bridge")More detailed output
geocode("Golden Gate Bridge", output="more")Reverse Geocode
revgeocode(c(-122.4194,37.77493), output="more")Geocode an address
geocode_ouput <-geocode("2625 Dana St, Berkeley CA, 94704", output="more")“Batch” Geocode
Geocode a set of places at once.
Usually means you upload the entire file to the geocoder.
This is much faster but not supported by most online services
With ggmap::geocode we serial process the set in one command.
Read in file of addesses
# File of addresses
address_data <- read.csv("address_data/oak_liquor_stores.csv", stringsAsFactors = F)
# Take a look
head(address_data)## id name street city state zip type
## 1 1 Wah Fay Liquors 2101 8th Ave Oakland CA 94606 p
## 2 2 Vision Liquor 1615 Macarthur Blvd Oakland CA 94602 p
## 3 3 Souza's Liquors 394 12th St Oakland CA 94607 p
## 4 4 Tk Liquors 1500 23th Ave Oakland CA 94606 p
## 5 5 Quadriga Wines Inc 6193 Ridgemont Dr Oakland CA 94619 p
## 6 6 Bev Mo 525 Embarcadero W Oakland CA 94607 cFormat addresses
Many online services want a single column with the address data, like "2625 Dana St, Berkeley CA, 94704"
address_data$full_address <- paste0(address_data$street, ", " ,
address_data$city, ", " ,
address_data$state, " ",
address_data$zip)
# Take a look
head(address_data, 3)## id name street city state zip type
## 1 1 Wah Fay Liquors 2101 8th Ave Oakland CA 94606 p
## 2 2 Vision Liquor 1615 Macarthur Blvd Oakland CA 94602 p
## 3 3 Souza's Liquors 394 12th St Oakland CA 94607 p
## full_address
## 1 2101 8th Ave, Oakland, CA 94606
## 2 1615 Macarthur Blvd, Oakland, CA 94602
## 3 394 12th St, Oakland, CA 94607Ready to geocode
google_geocoded <- geocode(address_data$full_address, output = "more",
source = "google", key=mykey)## Source : https://maps.googleapis.com/maps/api/geocode/json?address=2101%208th%20Ave%2C%20Oakland%2C%20CA%2094606&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Warning: package 'bindrcpp' was built under R version 3.4.4## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1615%20Macarthur%20Blvd%2C%20Oakland%2C%20CA%2094602&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=394%2012th%20St%2C%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1500%2023th%20Ave%2C%20Oakland%2C%20CA%2094606&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=6193%20Ridgemont%20Dr%2C%20Oakland%2C%20CA%2094619&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=525%20Embarcadero%20W%2C%20%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=5403%20Foothill%20Blvd%2C%20Oakland%2C%20CA%2094601&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1200%2078th%20Ave%2C%20Oakland%2C%20CA%2094621&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=828%20Franklin%20St%2C%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=5913%20International%20Blvd%2C%20Oakland%2C%20CA%2094621&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=3210%20Harrison%20St%2C%20Oakland%2C%20CA%2094611&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1460%207th%20St%2C%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1333%20Peralta%20St%2C%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=3710%20Telegraph%20Ave%2C%20Oakland%2C%20CA%2094609&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=3293%20Lakeshore%20Ave%2C%20Oakland%2C%20CA%2094610&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=1647%208th%20St%2C%20Oakland%2C%20CA%2094607&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=3849%20Martin%20Luther%20King%20Jr%20Way%2C%20Oakland%2C%20CA%2094609&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=3900%20Grand%20Ave%2C%20Oakland%2C%20CA%2094610&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=7305%20Edgewater%20Dr%20%23D%2C%20Oakland%2C%20CA%2094621&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTw## Source : https://maps.googleapis.com/maps/api/geocode/json?address=%20350%20E%2018th%20St%2C%20Oakland%2C%20CA%2094606&key=AIzaSyDf-SZG8O4hj1c06VQ-k6hkBrOQyOFWrTwView results
Google returns a lot of metadata - see documentation
head(google_geocoded)## lon lat type loctype
## 1 -122.2449 37.79836 premise rooftop
## 2 -122.2237 37.80033 street_address rooftop
## 3 -122.2705 37.80262 street_address rooftop
## 4 -122.2350 37.78426 street_address rooftop
## 5 -122.1672 37.78434 premise rooftop
## 6 -122.2791 37.79590 street_address rooftop
## address north south east
## 1 2101 8th ave, oakland, ca 94606, usa 37.79972 37.79702 -122.2435
## 2 1615 macarthur blvd, oakland, ca 94602, usa 37.80168 37.79898 -122.2223
## 3 394 12th st, oakland, ca 94607, usa 37.80397 37.80127 -122.2692
## 4 1500 23rd ave, oakland, ca 94606, usa 37.78561 37.78291 -122.2336
## 5 6193 ridgemont dr, oakland, ca 94619, usa 37.78569 37.78299 -122.1658
## 6 525 embarcadero west, oakland, ca 94607, usa 37.79725 37.79455 -122.2778
## west street_number route locality
## 1 -122.2462 2101 8th Avenue Oakland
## 2 -122.2250 1615 MacArthur Boulevard Oakland
## 3 -122.2719 394 12th Street Oakland
## 4 -122.2363 1500 23rd Avenue Oakland
## 5 -122.1685 6193 Ridgemont Drive Oakland
## 6 -122.2805 525 Embarcadero West Oakland
## administrative_area_level_2 administrative_area_level_1 country
## 1 Alameda County California United States
## 2 Alameda County California United States
## 3 Alameda County California United States
## 4 Alameda County California United States
## 5 Alameda County California United States
## 6 Alameda County California United States
## postal_code postal_code_suffix neighborhood subpremise
## 1 94606 2007 <NA> <NA>
## 2 94602 1606 Glenview <NA>
## 3 94607 4249 Downtown Oakland <NA>
## 4 94606 5035 Rancho San Antonio <NA>
## 5 94619 3724 Caballo Hills <NA>
## 6 94607 3565 Downtown Oakland <NA>Add Geocoded results to input file
# Add geocoded points to input data frame
# - works because num rows & row order returned by geocoder the same
address_data$glon <- google_geocoded$lon
address_data$glat <- google_geocoded$lat
head(address_data, 3) # take a look## id name street city state zip type
## 1 1 Wah Fay Liquors 2101 8th Ave Oakland CA 94606 p
## 2 2 Vision Liquor 1615 Macarthur Blvd Oakland CA 94602 p
## 3 3 Souza's Liquors 394 12th St Oakland CA 94607 p
## full_address glon glat
## 1 2101 8th Ave, Oakland, CA 94606 -122.2449 37.79836
## 2 1615 Macarthur Blvd, Oakland, CA 94602 -122.2237 37.80033
## 3 394 12th St, Oakland, CA 94607 -122.2705 37.80262Map results with Leaflet
library(leaflet)
point_map <- leaflet(address_data) %>%
addTiles() %>%
addMarkers(lat=~glat, lng=~glon,
popup=(paste0(address_data$name, "</br>",
address_data$full_address)
)
)Display the map
point_mapESRI World Geocoding Service
ESRI World Geocoding Service
UC Berkeley has an ESRI Site licence
So we can use the World Geocoding Service for free.
This is a great value as geocoding is expensive!
ESRI World Geocoding Service
In order to access the World Geocoding Service you need to first get an API key.
ESRI calls this an access token.
Instructions for this are on page 15 of this document.
Let’s try it!
ESRI WGS Access Token
You can use the token below for this workshop.
Tokens are temporary
- By default, a token expires in
2 hours. - Add an expiration parameter when requesting a token to receive one that is valid for a shorter or longer period of time.
- The maximum lifetime of a token is
14 dayseven if you specify a longer expiration period.
ESRI WGS Token
ESRI WGS Token
Your token should look something like this:
my_esri_token <- "iNFc0fq6bGlj6mUfptHgEGEjVeKZ0T5EPNAkVjgxBotwDaXxgykmwDNR0AkEvPi8Sh0QESWgt0IhmsbbO5UA3H-b2_T5yzhn1fW--WwD2O-7V2gbGCEaF4jg38dUQ7LhpTBT_XqYy0vV7Mw_e2hWUA.."Functions for Geocoding with the ESRI WGS
Two functions: geocode_one and geocode_many
These functions are long and ugly.
You can reference them by sourcing this script.
Take a look at the file on your computer
source("./scripts/esri_wgs_geocoding.R")Geocode One Address
geocode_one("2625 Dana St, Berkeley, CA, 94704", my_esri_token,
postal = TRUE)## Loading required package: httr## lon lat score locName status
## 1 -122.2601 37.86202 100 World M
## matchAddr side addressType
## 1 2625 Dana St, Berkeley, California, 94704 R PointAddressGeocode Many
# Batch geocode your dataframe of addresses with the following function
esri_geocoded <- geocode_many(address_data$id, address_data$street,
address_data$city, address_data$state,
as.character(address_data$zip), my_esri_token)## Loading required package: rjsonTake a look at results
ESRI WGS also returns a lot of metadata
head(esri_geocoded, 3)## ID lon lat score locName status
## 1 2 -122.2236 37.80054 100 World M
## 2 4 -122.2351 37.78423 100 World M
## 3 1 -122.2448 37.79827 100 World M
## matchAddr side addressType
## 1 1615 MacArthur Blvd, Oakland, California, 94602 L PointAddress
## 2 1500 23rd Ave, Oakland, California, 94606 R StreetAddress
## 3 2101 8th Ave, Oakland, California, 94606 L PointAddressAdd ESRI WGS Results to input data
address_data <- merge(address_data, esri_geocoded[c("ID","lon","lat")],
by.x="id",by.y = "ID", all.x = T)
# Take a look
head(address_data, 3)## id name street city state zip type
## 1 1 Wah Fay Liquors 2101 8th Ave Oakland CA 94606 p
## 2 2 Vision Liquor 1615 Macarthur Blvd Oakland CA 94602 p
## 3 3 Souza's Liquors 394 12th St Oakland CA 94607 p
## full_address glon glat lon
## 1 2101 8th Ave, Oakland, CA 94606 -122.2449 37.79836 -122.2448
## 2 1615 Macarthur Blvd, Oakland, CA 94602 -122.2237 37.80033 -122.2236
## 3 394 12th St, Oakland, CA 94607 -122.2705 37.80262 -122.2705
## lat
## 1 37.79827
## 2 37.80054
## 3 37.80238Map geocoded data with Leaflet
point_map <- leaflet() %>%
addTiles() %>%
addMarkers(lat=address_data$glat, lng=address_data$glon,
popup=(paste0(address_data$name, "</br>",
address_data$full_address))
) %>%
addCircleMarkers(lat=address_data$lat, lng=address_data$lon,
color="black",fillColor="red",
popup=(paste0(address_data$name, "</br>",
address_data$full_address))
)Display the map
point_mapGeocoding Historic Locations
Good question!
Save output to file
You don’t want to re-geocode!
write.csv(google_geocoded,file="output/address_data_geocoded_google.csv",
row.names=FALSE)
write.csv(esri_geocoded,file="output/address_data_geocoded_esri.csv",
row.names=FALSE)Geocoding Results
Assessing Output Quality
Map the results
- Examine the range of coordinates
- CA: -124, 32, -114, 42
- Review output data & metadata
- specific to the geocoder
Geocoding Output
- With a little preprocessing most reliable geocoders will be able to geocode 80% or more of your addresses within a street block of the actual location.
- based on my my experience!
- assumes US addresses.
- Cleaning and standardizing addresses can be a lot of work!
- unlikely to get it perfect
Standardize Addresses
- provide all components
- remove unnecessary components
- remove duplicates
- remove extra spaces or commas
- remove odd characters like “#” “/”, “@”
- standardize capitalization
Standardize Addresses
- Intersections
- Corner of Main and Long Ave should be Main & Long
- Numbered Streets
- Fourth St should be 4th St
- Directional Prefixes
- North, No, N., etc should be N
- Apartment numbers and letters
- Remove them!
- Use PO Box
- unless you have the address!
Use Standard Abbreviations
| Use | For These |
|---|---|
| HWY | Highway |
| LN | Lane |
| DR | Drive |
| EXPY | Expressway |
Problems in Reference Database
- Incorrect street ranges
- Inaccurate or low quality features
Inaccurate feature attributes
- Missing streets
Address changes
Output Quality & Population Density
Questions?
Linking to Census Data
Linking to Census Data
The US Census Bureau has been collecting data about the US population since 1790!
Survey data from the decennial census and the 5 year American Community Survey are two of the most commonly used databases.
Publicly available census data are aggregated to census geographies.
Census Geographies
Census Data
Census data are linked to Census geographies by IDs called FIPS Codes or GEOIDs
Census FIPS Codes / GEOIDs
Linking points to census data
In order to link geocoded points to census data you need to get the census geographic FIPS codes for the points.
There are a number of ways to do this.
Important Note
Census geographies can change every 10 years due to changes in the population.
For example,
if the population of an area increases, census tracts can split
if the population of an area decreases, tracts can merge
This makes comparisons between censuses more complicated.
- You need to get the correct census identifiers for the year(s) of interest
US Census Geocoding Service
If you geocode with the US Census Geocoding Service you can get the census IDs from the geocoder.
Try that with “2625 Dana St, Berkeley, CA, 94704”
FCC Census Block API
Provides an programming interface for fetching the 2010 Block FIPS code
https://geo.fcc.gov/api/census/#!/block/get_block_find
https://geo.fcc.gov/api/census/block/find?latitude=37.85256200000
&longitude=-122.2736340&showall=true&format=json>FCC Block API
Source the script file to load the function
latlon2fips
Then try it with -122.273634, 37.852562
source("./scripts/fcc_latlon2fips.R")
# test one coordinate pair
latlon2fips(latitude=37.852562, longitude=-122.273634)## [1] "060014240011002"Get FIPs for all of our geocoded addresses
Apply the latlon2fips function to all rows in our dataframe
Use the coordinates returned by the Google Geocoding API
address_data$fips<- mapply(latlon2fips, address_data$glat,
address_data$glon)
# Take a look
head(address_data, 3)## id name street city state zip type
## 1 1 Wah Fay Liquors 2101 8th Ave Oakland CA 94606 p
## 2 2 Vision Liquor 1615 Macarthur Blvd Oakland CA 94602 p
## 3 3 Souza's Liquors 394 12th St Oakland CA 94607 p
## full_address glon glat lon
## 1 2101 8th Ave, Oakland, CA 94606 -122.2449 37.79836 -122.2448
## 2 1615 Macarthur Blvd, Oakland, CA 94602 -122.2237 37.80033 -122.2236
## 3 394 12th St, Oakland, CA 94607 -122.2705 37.80262 -122.2705
## lat fips
## 1 37.79827 060014055003004
## 2 37.80054 060014049003026
## 3 37.80238 060014030001005Get FIPS spatially
We can download geographic data from the Census
and then spatially join the geocoded points to the census geographic data
to get the FIPS codes for each point
Get FIPS spatially
We can use the tigris package to download census geographic data.
library(sp)## Warning: package 'sp' was built under R version 3.4.3library(tigris)## To enable
## caching of data, set `options(tigris_use_cache = TRUE)` in your R script or .Rprofile.##
## Attaching package: 'tigris'## The following object is masked from 'package:graphics':
##
## plottigris
Set tigris options & download data
options(tigris_class = "sp") # options are sp or sf
options(tigris_use_cache = F) # set to true to save locally
tracts2010 <- tracts(state = '06', county= '001', cb = F, year=2010)##
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|=================================================================| 100%tracts2000 <- tracts(state = '06', county= '001', cb = F, year=2000)##
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|=================================================================| 100%Plot the tracts
plot(tracts2000, border="red")
plot(tracts2010, add=T)
Make address points spatial
Use the sp library to convert the address_data to a SpatialPointsDataFrame
Then, set the coordinate reference system, or CRS to be the same as that of the census tracts.
address_data_sp<- address_data #make copy
coordinates(address_data_sp) <-c("glon", "glat")
proj4string(address_data_sp) <- CRS(proj4string(tracts2010))A word about Geographic Coordinates
The Google Geocoding API returns geographic coordinates referenced to the World Geodetic System of 1984 or WGS84.
Census geographic data are referenced to the North American Datum of 1983 or NAD83.
These two geographic CRSs can differ by up to 1 meter in the continental USA.
It’s up to you to decide if this could impact your spatial analysis.
Plot the tracts and points
plot(tracts2000, border="red")
plot(tracts2010, add=T)
points(address_data_sp, col="green")
Spatial Overlay
The sp::over function spatially compares the two input spatial objects and returns the data values for the census tract in which each point is located.
fips2000 <- over(address_data_sp, tracts2000)
fips2010 <-over(address_data_sp, tracts2010)
# Uncomment to take a look at the output
#head(fips2000, 3)
#head(fips2010, 3)Add the FIPS to the address dataframe
address_data$GEOID10 <- fips2010$GEOID10
address_data$CTIDFP00 <- fips2000$CTIDFP00
# head(address_data)Questions?
Discuss the 3 methods for getting the census FIPS codes for the geocoded points.
When geocoding
Using an API
Using spatial overlay
Fetching Census data
Fetching Census data
We can take this one step further and retrieve census data for the census tracts of interest.
We do this with the tidycensus package
Get Census Data
library(tidycensus)## Warning: package 'tidycensus' was built under R version 3.4.3# Load census key - Patty's key, apply for your own!
my_census_api_key <- "f2d6f4f743545d3a42a67412b05935dc7712c432"
# Make tigris aware of census key
census_api_key(my_census_api_key)## To install your API key for use in future sessions, run this function with `install = TRUE`.Identify state(s) and county(s) of interest
my_states<- c("06") # CA
my_counties <- c("001") # Alameda CountyIdentify ACS census variables
Our scenario of interest is to identify liquor stores in census tracts with a relatively high population of minors.
So, we want to find the census variables for total population and population under 18
cenvar_table <-load_variables(year=2016, dataset = "acs5", cache=T)
# Take a look at the data frame to find the names of census tables
# This will give you the table and variable codes
# Use "View(cenvar_table)" and filter within the table to get the codes for a specific variable
# View(cenvar_table) Census Variables
Indentify the tables and variables of interest in the 5 year ACS Data (2012-2016)
pop_total <- "B01001_001E"
pop_under18 <- "B09001_001E"Fetch Data of interest
pop_acs5_2016 <-get_acs(geography = "tract",
variables = c(pop_total,pop_under18),
year=2016, survey="acs5",
state = my_states, county = my_counties,
geometry = F)## Please note: `get_acs()` now defaults to a year or endyear of 2016.#head(pop_acs5_2016)Reshape the data
Load libraries
library(tidyr)## Warning: package 'tidyr' was built under R version 3.4.2library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.4##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionReshape the data
# Select the columnbs of interest
# and put `totpop` and `under18` in their own columns
pop2 <- pop_acs5_2016 %>%
select("GEOID","variable","estimate") %>%
spread(key=variable, value=estimate)
# Rename columns
colnames(pop2)<-c("GEOID10","totpop","under18")
head(pop2)## # A tibble: 6 x 3
## GEOID10 totpop under18
## <chr> <dbl> <dbl>
## 1 06001400100 3018. 387.
## 2 06001400200 1960. 335.
## 3 06001400300 5236. 922.
## 4 06001400400 4171. 655.
## 5 06001400500 3748. 500.
## 6 06001400600 1661. 233.Join population data to Address data
pop2$pct_under18 <- round((pop2$under18 / pop2$totpop) * 100, 1)
address_data2 <- merge(address_data, pop2, by="GEOID10", all.x=T)
# Take a look - what do you think?
#head(address_data2)Join Population data to Census tracts to map
We just joined the census data to the address points.
We can also join it to the census tracts.
tracts2010 <- merge(tracts2010, pop2, by="GEOID10")
# Take a look:
# head(tracts2010@data)Note: this is sp::merge so we do not reference the @data slot!
Map the data
quantColors <- colorQuantile("Reds", tracts2010$pct_under18, n=5)
point_map <- leaflet() %>%
addTiles() %>%
addPolygons(data=tracts2010,
color="white",
weight=1,
opacity=0.5,
fillColor= ~quantColors(pct_under18),
fillOpacity = 0.75,
popup = paste0("<b>Percent under 18:</b> ", tracts2010$pct_under18, "%")) %>%
addMarkers(data=address_data, lat=~glat, lng=~glon,
popup=(paste0(address_data$name, "</br>",
address_data$full_address)
)
)Display the map
point_mapSave the output data
write.csv(address_data2, file="output/address_data_geocoded2.csv", row.names=F)
#writeOGR(tracts2010, dsn="./output", layer="tracts2010", driver="ESRI Shapefile")Questions?
Getting Help
Review the documents and websites linked in this tutorial
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